13th IEEE International Conference on Knowlede Graph (ICKG-2022)
November 30-December 1, 2022
Orlando, FL, USA
ICKG 2022 Call for Papers
Aims and Scope
Knowledge Graph deals with fragmented knowledge from heterogeneous, autonomous information sources for complex and evolving relationships, in addition to domain expertise. The IEEE International Conference on Knowledge Graph (ICKG), provides a premier international forum for presentation of original research results in Knowledge Graph opportunities and challenges, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of Knowledge Graph, including algorithms, software, platforms, and applications for knowledge graph construction, maintenance, and inference. ICKG 2022 draws researchers and application developers from a wide range of Knowledge Graph related areas such as knowledge engineering, big knowledge, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high performance computing, and World Wide Web. By promoting novel, high quality research findings, and innovative solutions to challenging Knowledge Graph problems, the conference seeks to continuously advance the state-of-the-art in Knowledge Graph.
Accepted papers will be published in the conference proceedings by the IEEE Computer Society. Awards will be conferred at the conference on the authors of the best paper and the best student paper. High quality papers will be invited for a special issue of Knowledge and Information Systems Journal in an expanded and revised form.
- Paper submission (abstract and full paper): July 31, 2022
- Notification of acceptance/rejection: September 11, 2022
- Camera-ready deadline and copyright forms: October 1, 2022
- Early Registration Deadline: October 1, 2022
- Conference: November 30-December 1, 2022
Topics of Interest
Foundations, algorithms, models, and theory of Knowledge Graph processing.
Knowledge engineering with big data.
Machine learning, data mining, and statistical methods for Knowledge Graph science and engineering.
· Acquisition, representation and evolution of fragmented knowledge.
· Fragmented knowledge modeling and online learning.
· Knowledge graphs and knowledge maps.
· Knowledge graph security, privacy and trust.
· Knowledge graphs and IoT data streams.
· Geospatial knowledge graphs.
· Ontologies and reasoning.
· Topology and fusion on fragmented knowledge.
Visualization, personalization, and recommendation of Knowledge Graph navigation and interaction.
· Knowledge Graph systems and platforms, and their efficiency, scalability, and privacy.
· Applications and services of Knowledge Graph in all domains including web, medicine, education, healthcare, and business.
· Big knowledge systems and applications.
· Crowdsourcing, deep learning and edge computing for graph mining.
· Rule and relationship discovery in knowledge graph computing.
Topics of interest include, but are not limited to:
· Track01: Machine Learning and Knowledge Graphs.
· Track02: Reasoning with Knowledge Graphs.
· Track03: Knowledge Graph Analytics and Applications.
· Track04: Knowledge Graphs and NLP.
· Track05: Knowledge graphs for Explainable AI.
· Track06: Multimodal Knowledge Graphs.
· Track07: Social Network and Representation Learning.
· Track08: Knowledge Graphs for Cultural Heritage.
· Track09: Knowledge Graphs for Geospatial Information Systems.
· Track10: Domain Knowledge Graphs.
· Track11: Knowledge Graphs for Education.
· Track12: Big Knowledge Systems.
Paper submissions should be no longer than 8 pages, in the IEEE 2-column format, including the bibliography and any possible appendices.Submissions longer than 8 pages will be rejected without review. All submissions will be reviewed by the Program Committee based on technical quality, relevance to Knowledge Graph, originality, significance, and clarity. You can choose to identify a Track Topic number in your submission title (e.g., your_paper_title-Track01) during submission.
All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is confidential. There is no separate abstract submission step. There are no separate industrial, application, shortpaper or poster tracks. Manuscripts must be submitted electronically in online submission system. We do not accept email submissions.
Nitesh Chawla, University of Notre Dame, USA
Kui Yu, Hefei University of Technology, China
Ronen Feldman, Hebrew University, Israel
Peipei Li, Hefei University of Technology, China
Qing Li, Hong Kong Polytech University, China
Zeyi Sun, Mininglamp Technology, China
Ting Bai, Beijing University of Posts and Telecommunications, China
Chenyang Bu, Hefei University of Technology, China
More information about ICKG 2022 is at
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